A SYSTEMIC FUNCTIONAL ANALYSIS OF ARTIFICIAL INTELLIGENCE SELF REPRESENTATION IN CHATGPT AND GEMINI RESPONSES
Keywords:
Generative Artificial Intelligence; ChatGPT; Gemini; Systemic Functional Linguistics; Transitivity Analysis; Self-Representation; AI DiscourseAbstract
Large Language Models (LLMs) have changed human-computer interaction by allowing conversational systems to converse using natural language and first-person self-reference. As these systems become more ubiquitous in education, professional settings, and everyday communication, it is critical to investigate how people use language to form their identities. This study examines the self-representation of ChatGPT and Gemini using Halliday and Matthiessen's Systemic Functional Linguistics (SFL), with a special focus on the transitivity system. The current research adopted a qualitative research method and purposively selected self-referential responses from both chatbots were analysed at the clause level to identify process types and participant roles. The result of this article demonstrates that both ChatGPT and Gemini systems determine their roles, skills, and functions and present themselves as active assistants primarily through Material and Relational processes. However, differences were found in their linguistic patterns furthermore ChatGPT presents a more conversational, transparent, and service-oriented identity, while Gemini shows a more formal and system-oriented identity, emphasizing purpose, collaboration, and design. There was very little application of existential and behavioural processes. The study highlights the value of transitivity analysis for understanding AI self-representation and linguistic identity construction in conversational AI.
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